Networked Capitalism: Unpicking the ventures of the PayPal Mafia

It started with this old article on Fortune.com, which told a fascinating story of Silicon Valley’s group successful tech entrepreneurs. It includes such iconic figures of the tech world as Elon Musk, Peter Thiel, Max Levchin among others, and is otherwise known as the PayPal Mafia.

There are are 24 members in this powerful group, all of whom met while working at PayPal and have since collectively been involved in hundreds of tech startups and companies as founders, investors, executives, and advisors.

However, it wasn’t the success these tech whizzes achieved in their various ventures that fascinated us, it was that these notable PayPal alumni still start, fund, and work on companies together. Three of the “mafiosi” co-founded YouTube, another trio co-founded Yelp, and two were involved in the establishment of LinkedIn.

Researching more around the subject, we found quite a few excellent articles that detailed the many different ventures that members of the PayPal Mafia have set up together. But, while there were plenty of articles, there was a lack of compelling visualisations which showed all of the companies these entrepreneurs touched, or even showing just how closely-knit the network of PayPal Mafia are.

That’s when we knew what we were going to visualise – all the ways in which these notable alumni of PayPal are all connected together via the plethora of companies they founded, funded, led, and advised.

Choosing a visual execution

When trying to show connections between objects, a network diagram is the visual that instantly comes to mind. We toyed with a few other types of visualisations: some focusing on the development of this network over time, others placing emphasis on individuals in this “mafia” group – both were rejected rather quickly and we decided to go with the network graph.

The network diagram, however, didn’t resolve all our challenges. Whereas most network graphs are simply connections (edges) between points (nodes) of the same type, we were stuck with nodes of two distinct categories: entrepreneurs (the PayPal Mafia) and companies (their joint and solo ventures). It would be odd and incorrect to place them amidst each other on the same graph.

Not only did it show how you can position different types of nodes on the same graph (nations and clubs), it also confirmed that it was possible to visualise this type of data in an interactive way, well-suited for exploration. So, this was the approach we took: position members of the PayPal Mafia in a circle, and show their joint ventures as bubbles within that circle, all connected together.

Gathering data and pulling out stories

We started this process by tallying up all the members of the PayPal Mafia into a list, and then running the names through CrunchBase - a great source for data on startups, founders, and the tech world in general.

We were interested in seeing all the companies that each member of the PayPal Mafia founded, invested in, led as an executive, and advised as a board member or adviser.

Cross-referencing our CrunchBase findings with articles in tech media and official websites of the companies in question, we collated a dataset that included all the companies each PayPal ‘mafiosi’ founded (or co-founded), invested in, led (is or was in an executive or director role), and advised (either as a board member or adviser).

Digging around for stories yielded three core insights from the dataset:

A lot of companies we found were household names, like LinkedIn and YouTube

The sheer degree of collaboration between members of this group was astounding

Aside from tech startups, their joint ventures included more curious projects, such as the film Thank You For Smoking and the lobbying group called FWD.US

We were even more fascinated to find out that the influence of Elon Musk, Peter Thiel, and co. went even deeper. Not only did they have a hand in setting up some of the most successful startups in the Silicon Valley, they have also started their own venture capital firms such Mithril and Founders Fund. Thus, investments made by those funds on their behalf gave the PayPal Mafia a piece of the action in many more ventures.

Creating the visualisation

In creating this data visualisation, three things were important to us:

This time, due to the complexity of the chosen execution, we took a different route. Instead of the designer spending hours and hours mocking up every single node and edge on the graph, we went straight to interactive prototyping, and styled it later. It worked like a charm.

Seeing the nodes and connections interact with each other helped us get a clear sense of how they would work in the browser straight away - a luxury you don’t have when you start with static designs.

Using d3.js - a JavaScript charting library developed by Mike Bostock, we plotted our data using a modified force-directed graph with a custom radial fixed positioning for the mafiosi nodes. We used Modernizr to estimate size of the screen and programmatically load a modified version code depending on the user’s screen size.

As evidenced by Andy Kirk’s The Little of Visualisation Design series, every piece of data visualisation needs a few small, but crucial decisions to make it work and tie it all together.

Working with this reasonably complex dataset, we strived to ensure that the most interesting insights it has to offer didn’t get lost in the dense visualisation. In our case, there were a few decisions we had to make.

We brought in photos of each member of the PayPal Mafia to make the piece more human and proverbially, put faces to the names, showing everyone on the same chart.

Eager to convey the sheer density of the PayPal Mafia network, we drew every single line between each person and each venture, and then spent a long time calibrating its thickness and colour.

To make notable companies stand out, we pulled the data on total amount of investment from CrunchBase and sized the bubbles accordingly. In addition, we manually annotated the most recognisable names to encourage users to click onto them to explore the graphic.

In order to visually distinguish between different roles the “mafiosi” had in different companies, we circled their photos with strokes of colour based on what role they fulfilled: founder, investor, executive, or adviser. The same colour scheme was applied to companies when a given person was selected.

To make our stories shine, we borrowed the scrollytelling mechanism from the NY Times article that highlighted the relevant parts of the graph as you moved through the story. Keen to allow the more curious users to explore our data themselves, we enabled exploration at the beginning and at the end of the story.

To that end, users could explore the network graph by clicking on either the entrepreneur or the company to drill down into the more granular information. Clicking on an entrepreneur, showed all the companies that the entrepreneur has been involved in. For example, Peter Thiel founded four companies, was an Advisor at three and invested in 53 companies.

Clicking on a company revealed members of the PayPal Mafia that had a hand in it. For instance, Reid Hoffman invested in Facebook, Peter Thiel serves on its board, and Yishan Wong was its Head of Engineering for a period of time.

Having an interactive prototype also allowed us to immediately see how this dataviz would work on smaller screens. It quickly became very clear that we won’t be able to achieve a smooth interactive user experience on mobile, so we used static image versions of our story views instead.

As for the exploration mode, we created custom images for each member of the PayPal Mafia, showing which ventures they are part of, and which other members of the mafia they are connected with. From a coding perspective, it was a completely different layout, with a carousel powered by Slick.js.

Volodymyr Kupriyanov is a freelance researcher and data journalist based in Copenhagen. With a background in sociology and data analysis, he has spent the better part of the last 5 years helping transform data into compelling visual stories.

Credits

This data visualisation was commissioned by Fleximize and created by Distilled. Individual credits:

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